Mining Graph Data

This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you’ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets.

There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.

Table of Contents

Chapter 1 Introduction

Part I GRAPHS.
Chapter 2 Graph Matching—Exact And Error-Tolerant Methods And The Automatic Learning Of Edit Costs
Chapter 3 Graph Visualization And Data Mining
Chapter 4 Graph Patterns And The R-Mat Generator

Part II MINING TECHNIQUES.
Chapter 5 Discovery Of Frequent Substructures
Chapter 6 Finding Topological Frequent Patterns From Graph Datasets
Chapter 7 Unsupervised And Supervised Pattern Learning In Graph Data
Chapter 8 Graph Grammar Learning
Chapter 9 Constructing Decision Tree Based On Chunkingless Graph-Based Induction
Chapter 10 Some Links Between Formal Concept Analysis And Graph Mining
Chapter 11 Kernel Methods For Graphs
Chapter 12 Kernels As Link Analysis Measures
Chapter 13 Entity Resolution In Graphs

Part III APPLICATIONS.
Chapter 14 Mining From Chemical Graphs
Chapter 15 Unified Approach To Rooted Tree Mining: Algorithms And Applications
Chapter 16 Dense Subgraph Extraction
Chapter 17 Social Network Analysis